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    Failure probability prediction based on condition monitoring data of wind energy systems for spare parts supply
    The feasibility of maintenance processes relies on the availability of spare parts. Spare part inventory planning is capital intensive. It is based on demand forecasting, which possesses a high potential in reducing inventories. Even if condition monitoring systems are installed in technical systems, condition monitoring information is barely used to predict the failure probability of units. Therefore, an enhanced forecast model, which integrates SCADA information, has been developed. This leads to more accurate spare part demand forecasts. The approach presented in the paper is based on data mining, the proportional hazards model (PHM) and a binomial distribution. It has been validated with maintenance data of wind energy systems.
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    Error mapping of rotary tables in 4-axis measuring devices using a ball plate artifact
    Rotary tables as components of 4-axis measuring devices provide a quick rotary positioning in geometry measurements, but the non-trivial error motions need to be determined and compensated. This paper presents a new solution for the error mapping, which calibrates the rotary table, the 3-axis measuring device and the artifact (circular ball plate) simultaneously. Multi-measuring and approximation techniques are introduced to identify and quantify the error motions based on obtained point clouds. A mathematical model covers all the deviation sources, occurring at the movement of a rotary axis. Simulations and experimental results verify and validate the solution. The simple and flexible calibration setup together with the developed evaluation procedure enable a compensation of rotary table/axis deviations in a wide application field.
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